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A Comprehensive Survey on Federated Learning in the Healthcare Area: Concept and Applications

Computer Modeling in Engineering & Sciences 2024年第9期140卷 2239-2274页

作者： Deepak Upreti Eunmok Yang Hyunil Kim changho seo Department of Convergence Science Kongju National UniversityChungcheongnam-doGongju-si32588South Korea Department of Information Security Cryptologyand MathematicsKookmin UniversitySeoul02707South Korea Department of Information and Communication Engineering Chosun UniversityGwangju61452South Korea Basic Science Research Institution Kongju National UniversityChungcheongnam-doGongju-si32588South Korea

Federated learning is an innovative machine learning technique that deals with centralized data storage issues while maintaining privacy and *** involves constructing machine learning models using datasets spread across several data centers,including medical facilities,clinical research facilities,Internet of Things devices,and even mobile *** main goal of federated learning is to improve robust models that benefit from the collective knowledge of these disparate datasets without centralizing sensitive information,reducing the risk of data loss,privacy breaches,or data *** application of federated learning in the healthcare industry holds significant promise due to the wealth of data generated from various sources,such as patient records,medical imaging,wearable devices,and clinical research *** research conducts a systematic evaluation and highlights essential issues for the selection and implementation of federated learning approaches in *** evaluates the effectiveness of federated learning strategies in the field of *** offers a systematic analysis of federated learning in the healthcare domain,encompassing the evaluation metrics *** addition,this study highlights the increasing interest in federated learning applications in healthcare among scholars and provides foundations for further studies.

关键词： Federated learning artificial intelligence machine learning privacy healthcare

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Improving the Detection Rate of Rarely Appearing Intrusions in Network-Based Intrusion Detection Systems

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Computers, Materials & Continua 2021年第2期66卷 1647-1663页

作者： Eunmok Yang Gyanendra Prasad Joshi changho seo Department of Financial Information Security Kookmin UniversitySeoul02707Korea Department of Computer Science and Engineering Sejong UniversitySeoul05006Korea Department of Convergence Science Kongju National UniversityGongju32588Korea

In network-based intrusion detection practices,there are more regular instances than intrusion *** there is always a statistical imbalance in the instances,it is difficult to train the intrusion detection system *** this work,we compare intrusion detection performance by increasing the rarely appearing instances rather than by eliminating the frequently appearing duplicate *** technique mitigates the statistical imbalance in these *** also carried out an experiment on the training model by increasing the instances,thereby increasing the attack instances step by step up to 13 *** experiments included not only known attacks,but also unknown new *** results are compared with the existing studies from the literature,and show an improvement in accuracy,sensitivity,and specificity over previous *** detection rates for the remote-to-user(R2L)and user-to-root(U2L)categories are improved significantly by adding fewer *** detection of many intrusions is increased from a very low to a very high detection *** detection of newer attacks that had not been used in training improved from 9%to 12%.This study has practical applications in network administration to protect from known and unknown *** network administrators are running out of instances for some attacks,they can increase the number of instances with rarely appearing instances,thereby improving the detection of both known and unknown new attacks.

关键词： Intrusion detection statistical imbalance SMO machine learning network security

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关于曲线的L(2,l)-标注(英文)

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长春理工大学学报（自然科学版） 2005年第1期28卷 120-124页

作者：徐昌浩金锡佑公主市国立大学应用数学系韩国韩世大学信息处

假设曲线G=(V,E),G的L(2,1)-标注是方程式f:V(G)→[0,∞],那么如果(x,y)∈E,则|f(x)-f(y)|≥2,如果dG(x,y)=2,则|f(x)-f(y)|≥1,此处的dG(x,y)是G曲线中x和y之间最短的距离。L(2,1)-标注数字λ(G)是最小数字m,那么G则有最大{f(v)|v∈V}=m的L(2,1)-标注f。格里戈斯和叶[6]及山凯[2]曾通过各种曲线对这个问题进行过研究。本文中我们提高了弦曲线λ(G)的已知上界并提供了曲线λ(G)的第一个上界。

关键词：弦曲线置换曲线

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